Jeff Standridge:
Welcome to another episode of Innovation Junkie. This is Jeff Standridge.
Jeff Amerine:
Hey, and this is Jeff Amerine. Looking forward to today’s episode, Jeff.
Jeff Standridge:
Yeah, looking forward to it as well. We have a repeat guest. He’s the author of Running Lean: Iterate from Plan A to a Plan That Works. Also the author of Scaling Lean, which came out about 2016, I believe, Mastering the Key Metrics for Startup Growth. He’s the CEO of LeanStack and is a fabulous, fabulous thinker in the space of innovation and startups and Ash Maurya. It’s great to have you with us today.
Ash Maurya:
Thanks for having me. It’s a pleasure to be back.
Jeff Standridge:
Yeah. So why don’t we start, uh, by the way, uh, episode one, I’m sorry, season one episode 23 was Ash’s episode. Uh, so a couple of years ago on lean innovation, if you want to, uh, spend a little time listening to that, we encourage you to go back and do that. But, but Ash, bring us up to speed. Tell, tell the team a little bit, or the listeners rather a little bit about what you do, and then I want to get into where you’re focused today and what’s top of mind for you.
Ash Maurya:
Sure, so I probably said some of the same thing last time around as well. So I’ve been an entrepreneur at heart. What got me particularly excited was trying to make sense of the early stage of product development. So a lot of my projects and many of the founders I work with kind of share the same sentiment. Everything seems amazing at the outset. Takes about a year, a year and a half, sometimes two years to figure out if it’s going to work or not. And shortening that cycle time is really what I’m all about. So trying to find more systematic processes, thinking processes, validation processes that we can use to get to that product market fit point faster.
Jeff Standridge:
Very good. And so, so how does Lean Stack and how do you do that other than I know I follow you on LinkedIn, you’re prolific there, you put out great content, but how do you go about doing that other than just writing?
Ash Maurya:
So some of it was trying to do some of the processes that I talked about. So working on business modeling, so designing a business model at the outset, and then going through a more iterative validation process that prioritizes what’s riskiest before typically what’s easiest. So that’s kind of at a high level what the process looks like. But on a more practical level, we build software-coaching products both for founders to use. We also train accelerators and university educators on this way of working. And so just trying to build more structure and as I said, more repeatability to what’s otherwise a very chaotic early-stage journey.
Jeff Standridge:
Yeah. Well, most of our listeners know that in addition to Innovation Junkie, we have Startup Junkie and Conductor, which are early-stage, uh, consultancies working with entrepreneurs and aspiring entrepreneurs, and three conversations today with early-stage tech founders. And I went back to the staple of running lean, the lean canvas. And, I even point them to your, to your 20-minute video, a lean canvas in 20 minutes, and guide them through that process, but it has become pretty much a repetitive process for me and I think not just for me, but for the industry as well.
Ash Maurya:
Great to hear.
Jeff Amerine:
Ash, a kind of a follow-up for you there. You’ve been at this now, man, it’s got to be more than 10 years, right, since the initial release of the book. What’s the installed base of people that are advocates or users of the running lean methodology? I know we don’t just use it in training startup ventures and new entrepreneurs. We’ve used it in best practice processes with product development in Fortune One and in very large enterprises as well, where they’re trying to think, how can we be more agile? How can we be quicker to market? What’s that installed base of activity look like now? Do you know?
Ash Maurya:
So it’s really hard to come up with an exact number. So we, as I said, run some software so we can go off of those counts and then just extrapolate from there. But I would easily place it in like three, four million. And that’s even just a rough guess. I don’t know if it’s higher, maybe higher. But to your point, I’m always happy to see that Lean Canvas has a life of its own. A lot of people know Lean Canvas, but don’t know me, and that’s how I wanted it to be. It’s spread and definitely gets used in, as you said, all kinds of environments from Fortune 500 type environments to even what’s been very inspiring to see is even high school students. I’m actually meeting with a high school student in a couple of days that wants to do a quick conversation around his idea as lead campus.
Jeff Amerine:
Yeah, the fundamentals translate extremely well to K through 12. And we’ve done a bunch of that too, where we’ll even have junior high students in there. And I normally preface it with, you’ve learned scientific method. That’s all this is, is a framework for asking good questions and validating or invalidating your hypothesis in an iterative way. And they get it. And I mean, they do remarkably well at it. That’s the beauty of any good methodology is simplicity.
And this is simple enough that anyone can understand it if they’ve had some basic education.
Jeff Standridge:
Yeah, we’re at, we’re about to use it for our sixth iteration of a healthcare innovation sprint for college juniors, seniors, and immediate post-graduates. We’ve been running that now for six years. This is our sixth year of running it and, uh, and it’s a week-long intensive. And the, it really focuses around, you know, taking that prob identifying a problem and then leveraging the lean canvas to kind of flesh through that, that problem. So good stuff. So where are you spending your time today?
Ash Maurya:
Okay. So a lot of the work, again, if I go through some of the high level ideas behind the framework, it almost seems commonsensical. But the challenge, of course, with commonsense is that it’s hard to find it, you know, practice all that rigorously in the real world. And so a lot of what I spend my time doing is trying to, you know, work with coaches, but also work with early stage teams to understand where some of those hangups are.
And I break it down to essentially just old ways and new ways of working. It’s not that people don’t understand some of the merits of starting with problems before solutions, but our brains, these cognitive biases just keep tricking us into falling in love with the solution. And sometimes I find teams well-intentioned will go and do survey-based validation, which again leads to false positives. So that’s kind of where I am now. It’s like trying to find some of the most common pitfalls and bringing in some of the science of habits and reward loops and things of that nature to bring that into the coaching, early stage coaching and practice side of things. And then the other big theme, of course, we had the big macro event of a chat GPT hitting the scene. So everyone’s been wondering, you know, what’s the future of AI and startups and innovation and a lot of things out there and we have been tinkering on our own kind of in that.
Jeff Amerine:
What’s your take? I mean, as you think about, there’s a lot of sort of mechanical activity that you have to do as part of a lean canvas approach, as part of customer discovery, as part of design thinking, all those methodologies. What’s, in your judgment, what impact is AI, ChatGPT, and the various tools going to have on how we go about doing some of this?
Ash Maurya:
Yeah, so first I would just take a step back and say, whenever we see something new like this come, we go through the Gartner hype curve. There’s going to be this huge hype of like everything is possible. And so when this first hit the scene, I saw services come up where you could just ask the AI to pretty much do a startup for you, generate my lean canvas, write some code for me, pull up a landing page. And people thought they were just going to launch and build successful things. And I’m not saying that’s, you know, never going to be possible, but I think where we are at this stage, I find that the more practical use is going to be more in predictive AI than generative AI. I think generative AI is great for some of the content things we’re seeing, but startups are inherently hard, even with good ideas. And so I think where these tools get used, we are looking at kind of two areas. So one of it is simulations. So where I talked a bit earlier about how some of this stuff is common-sensical, but people get tripped up. So if we can imagine some kind of a co-pilot or a coach where team creates a lean canvas and we can apply some well-established stress tests on viability or like problem arguments, how well the argument is being made, there can be some guidance there to tighten up the business model story. But where we’ve the other place where we spend, have spent quite a bit of time tinkering on our own is in the customer interviewing arena. And there again, we don’t want hallucinations, we don’t want creative thinking, we really want just good summarization of insights, what was said in the conversation. And so I think that’s where I find the real ready kind of prime application for something like this.
Jeff Standridge:
So help me understand that when you say in the customer conversation, is it AI to, to synthesize that information, analyze and synthesize that information, or is it in the creating of the questions themselves or.
Ash Maurya:
Yeah. So again, the way that we tend to coach teams is to do more open-ended questioning. So we provide, you know, I sometimes tell them the best script is no script and that usually stresses people out, but you know, you give them a starting script, just like this interview, you know, we didn’t really have a set of questions. We’re not going off a script. We kick things off and then we just go with the flow. So those are the best interviews, the best customer interviews. But where then people struggle is taking those interviews and extracting insights off of them. So one of the things we have been playing around with is we have come up with a tuned model that’s built on a specific kind of interview, if you’re familiar with jobs to be done or switch-type interviews. And there are a few others that we have trained the model on. So if you feed in a transcript or better yet, a recording of the conversation, we can extract it out and extract a summarized story off of it. And so that’s what models like that are very good. So no hallucination. You know, we even tell people when you’re running these interviews, pretend you’re a detective looking for, uh, causality in the, in the statements. And again, that’s what, you know, machines are excellent at doing. If you just have them do that and not get too creative. That’s where I go. You know, that’s where I went with the predictive versus generative. So that’s one side. And then the other place we see teams struggle with is in the insight mapping. So you have done 10, 15 interviews.
How do you, without bias, summarize those interviews, group them into clusters? How do you prioritize top problems within each cluster? How do you build up tables where you’re coming up with factors? So what might have made a customer to act a certain way on a scale of one to five? This is a lot of tedious work. And we would put our teams through that, and they would just be very exhausting. But now, at least for a first-person, we call it a co-pilot for a very conscious reason.
And I’ll get to that, but now you can kind of feed it 10 interviews, and less than a minute later you’ve got data that you can now begin reviewing and then tweaking and adjusting. So that’s the big leap. And again, the reason I say we call it a copilot and we are kind of cautious with our teams is the danger with giving tools like these to teams is they may suspend their own critical thinking. So just go run interviews and then listen to what the model says. And that’s again where we’re back in. Yeah, so we want to try to be cognizant that can happen and try to kind of work with that.
Jeff Standridge:
Very good. So, when you talk about more predictive uses beyond this synthesis summarization, what are you thinking for the future in that regard?
Ash Maurya:
So I mean, already on the more kind of well-defined problem sets, like on the coding side of things, and this is where GitHub and some of the others have done some amazing work there, even our own dev team relies on tools like that, I think we’re just going to see a kind of a new renaissance of on the build side of products, especially digital products, and even other obviously other arenas. But I think those are going to be where again, there’s going to be a big impact on cycle times in terms of prototyping or kind of thinking through models and pitting them in those simulations that I was talking through. So those arenas I think are again prime for usage there.
Jeff Standridge:
What about adoption? You know, I, we were talking with a guest, um, on another episode where he was looking at folks in his particular discipline and, and the, the number of people who still aren’t comfortable, confident, um, or really have had very, very little interaction with, uh, with AI or even ChatGPT is, uh, pretty astounding. Um, you know, I’m wondering about what’s what’s ultimately going to drive adoption in the world of startups, scale-ups, and growing businesses.
Ash Maurya:
Yeah. So I mean, as with anything, there’s going to be the bell curve of your early adopters. And again, with that hype, I often, at least with the startups that I’m seeing, I’m almost seeing the over-adoption side of the problem, which is, you know, startups have become so competitive. Everyone’s doing a startup, it seems like these days on the side. And so many folks are, at least when I think it’s starting to calm down, but at the very outset, I found many people almost overusing AI to sometimes a fault, which is, as I said, generate canvases and tell me if my idea is good, those types of things, which I think is maybe not a very productive use of those technologies, at least at this stage.
Jeff Amerine:
And how much of your time are you spending thinking about the new products and sort of this direction things are headed versus coaching and sort of the existing state of understanding? How are you splitting your time these days?
Ash Maurya:
Sure. So I would say 80% of it is really trying to systematize the coaching side of things. But just by proxy of that, I get exposed to lots of teams that are building new things. And so I guess just by that immersion, maybe it’s even 20 and sometimes even 30% of what I get to see is what many startups are working on and how their new products and what they’re doing.
But definitely, the focus is trying to get, as I said, more structure to a lot of this and see if this can be readily scaled, not just in North America, but we’re working also with teams and coaches like in other parts of the world as well, Africa, Asia. So it’s always interesting to see how there are subtle differences, but at a macro level, we’re all the same. We tend to make the same mistakes, fear the same things. So the same is true in startups.
Jeff Amerine:
You’ve been a prolific author. Is there another book forthcoming?
Ash Maurya:
Since I have been working a lot on coaching, so I’ve been half joking, but it’s almost semi-official now. So I did Running Lean, as you kind of mentioned at the outset, Scaling Lean. And so there is a third book that I’m starting to put together, which would be Coaching Lean. So it would be more written for either coaches, educators, but also team leads. Again, if we just look at how Agile grew when Agile started, they were, there were coaches who came and helped. I remember a coach helping my team kind of learn the practice and then eventually the team becomes self-sufficient. And so team leads learn this. And so I think that book will, if all this keeps moving at the pace it is, we want kind of teams to be able to self-regulate and hold each other accountable. And that would be what this book aims to get at.
Jeff Standridge:
Well, that certainly will be on my shelf when it’s out because the number of coaching engagements that we do in a month, much less a year, I could probably be a little more lean in doing that. So, just tell our listeners a little more about what an engagement with LeanStack looks like. You know, who’s your ideal customer? You know, and when you connect with one of those, what does an engagement look like?
Ash Maurya:
Yeah. So there was actually a life of LeanStack kind of in the early day. So after the tools and the second book got written, I guess maybe even I’ll start with the first. After the first book got written, there were still many unanswered questions. You know, Lean Canvas was a great way to create a blueprint. But always the question I would get from teams is I’ve gone and done early validation, but now my stakeholders and my investors want to see the Excel Magic spreadsheet forecast. And that’s why I wrote Scaling Lean and then there introduced a more metrics-driven model. So that was what the first four or five years of the company was about was me trying to make sense of the models that would help make sense of the early stage, both from a business model perspective, but also traction perspective. And a lot of the work we were doing was very founder facing. So I was running a lot of boot camps and workshops and doing a lot of one-on-one coaching, even with teams.
Really, because in the back of my mind, I wanted to see, one, does this work in other domains other than software, which is where I come from? And then two, if it does, how repeatable it is, how scalable it is. So those were questions I wanted answered. And then about five years into that journey, we did a switch and we built LeanStack, and that was a platform that essentially packages a lot of the content and tools. And we now license that out to different entities. So our ideal customer today would be a startup accelerator or a university that’s running an entrepreneurial program that wants tools and some content to jumpstart what they’re doing. So that’s who we work with. Because everything that I’ve built came out of my own tinkering, I’ve kept around another brand. So this Lean Foundry is essentially a brand that I run which is still founder-facing. And that’s where I continue to tinker and do this AI stuff I was talking about happens there. And many things don’t see the light of day, but if it kind of passes our basic tests and it’s something useful and valuable, then we push it up. Push it up.
Jeff Amerine:
You know, one question I’d have for you is, there’s this sort of lineage of how business canvas and lean canvas is developed. And we think about the work Steve Blank did, and we think about Eric Ries, and we think about you. And all those models still exist out there. If somebody is looking to make a choice about which to adopt, they know they need to do something for validation. They know they need to do something that’s gonna be iterative, and it’s gonna have hypothesis testing. What advice would you give them? And I know clearly you may have your own biases, but I think objectively, how would you, how would you position where you sit versus those other two pieces?
Ash Maurya:
So at the end of the day, even if you just take the two canvases, I tell folks that they are both one page, they’re both small enough, kind of look at them and use the one that kind of fits with your model. And what I tell a lot of folks is that even when I was doing Lean Canvas, it wasn’t trying to create a better competitor to the business model canvas. It was really coming at it at a different paradigm. So there is a place if you have an existing business and you look at the business model canvas, you can look at existing key resources and key partnerships and start asking, we’ve got these assets, how can we be creative in how we use these resources to do something different and new? Where I was coming from in my own projects and where a lot of early stage founders are is they don’t have key partners or key resources. They start with nothing. It’s a blank canvas. And so, they are the lowest common denominator from my perspective is who’s your customer and what problem are you solving? If you can get that, and that’s what I’ve even built a leaner canvas to say, even if you just get that foundation solid with evidence, if you can point me to a big enough starving group of people that want a problem solved so bad, you’re already way ahead than most people. So even if you just start with those two boxes, you’re way ahead. So that’s kind of where lean canvas comes from. So usually what I will tell folks is use both.
But at the end, I think those that start to see the value in beginning with problems tend to gravitate more towards the in-canvas and then those that are more strategic or have an existing, as I said, set of resources that they are looking to redeploy in some innovative way may gravitate towards the other. Beyond that, I would say both tend to still have a similar prioritization next step, which is figure out what’s riskiest and then start running experiments. And then even there, there are tactical differences and how we might go about things. But I would almost just say for folks out there, start small and then explore both for a little while because they’re both lightweight models and pick the one that seems to give the most ahas or the most impact for them.
Jeff Amerine:
Very good.
Jeff Standridge:
So, so now I’m going to put you on the spot. Um, I mentioned earlier in the, in the episode that I, um, that I follow you pretty avidly on, on LinkedIn. And you made a post a couple of weeks ago that said, uh, the, the title of the post was a lean canvas is not. Enough to replace a business plan. Tell us your thoughts there.
Ash Maurya:
Sure. And it goes back to what I said a few minutes ago, is that when the initial Lean Canvas came out, it was a good way to summarize what would be the words in a business plan. So the chapters on your go-to-market, or who your customers are, and how you’re going to make money. We’re not writing pages and pages. We’re putting them on these building blocks on a single page. Because at the end of the day, you have to be concise. If you can’t, I often say, if you can’t communicate your idea in less than five minutes, or summarize it on a page, it’s still too complex in one’s head. So that was what the promise was and what it did. But the bit that the business plan also has is the number side of the story. And many stakeholders want to see the forecast model. They want to see, is the idea big enough? And that’s where the lean canvas tends to fall short. There is a key metrics box, but it’s still just too small a box to get that story out.
So that’s where the second model, the traction model or the traction roadmap got built, which is almost like a zoom in into key metrics. And I find if you can present both those things to investors, then you can tell the overall business model story along with a hockey stick picture with milestones, with actual numbers and assumptions, customer size and how many customers. You may not know what you’re going to build, but at least you can make a promise of how much traction you are going to come and deliver at certain time.
Jeff Standridge:
So do you find that it’s better when you’re coaching with your clients to start with the Lean Canvas and build a business plan or to start with a business plan and really focus in on getting the essence of that plan in a Lean Canvas?
Ash Maurya:
Yeah, so I would say starting with a lean canvas, kind of that leaner approach is the preferred way simply because the bigger the document, the more folks kind of get lost in the analysis paralysis. And there’s not enough that in those early stages, it’s so fictional. At that point, you might as well, even if I would even say, you know, the best scenario is you don’t ever need like a fully formalized business plan. If you can. If you can, with a lean canvas and some of these lighter-weight traction models, go and demonstrate traction, usually investors will sit you down and just be happy with a 10-minute slide deck, kind of a pitch. So you may not even have to write a formal business plan. But if you’re in an environment where you do, it’s helpful to think of the lean canvas almost as an outline of your novel of a business plan that you’re eventually going to have to deliver.
Jeff Amerine:
That’s very consistent with that’s where I was going to say that’s very consistent with the approach we’ve taken. You know, when I try to describe the modern business plan, it’s a fully fleshed-out lean canvas. It’s a hundred customer interviews that you’ve done using a lack of, of confirmation bias and asking good open-ended questions, it’s a 10 or 15-slide. PowerPoint presentation. Once you get to the point that you can articulate it.
Jeff Standridge:
Yeah.
Jeff Amerine:
It’s a financial model for sure that because you’re some are going to ask for that. And then it’s a good one-page executive summary. And that’s kind of the toolkit. A lot of times that will advise because nobody, you know, being on the receiving end of a thousand of inbound inquiries, I haven’t read a full business plan in 10 years, at least I might get through an executive summary. I can follow a pitch deck. So it’s just understanding the audience too, and the lack of time the audience has to review that kind of stuff.
Jeff Standridge:
So Ash, tell our listeners where they can find you and how best they can connect with you.
Ash Maurya:
Okay, so there are two places, I guess maybe three places. I am pretty active on LinkedIn, so I post almost daily. So if someone wants to get a taste of what this kind of thinking is all about, I’d probably say head on over there and find me there. And then there are two kind of sites that I run. So if you’re a founder looking to start something new, then head on over to leanfoundry.com, and that’s where there’s kind of tooling, there’s some early content you can get into. And then if you’re more on the coaching side of things or running an innovation hub or accelerator, then definitely check us out on LeanStack and see if there’s something there that we can do with the platform to maybe help with your endeavor.
Jeff Standridge:
Any parting words for our listeners?
Ash Maurya:
I don’t know if I shared this the last time, but I’m still kind of living by the mantra of love the problem, not your solution. So I find that in a lot of the early stage work, that’s almost the first mindset that people seem to, one, get excited by, but two, when they start practicing it, quickly realize that it’s the hardest one to practice. And so I completely empathize. It is a hard thing, but it’s one of those. I consider it, you know, the key activity in those early stages. If one can get good at problem identification and prioritization, that’s more than half the journey.
Jeff Standridge:
Very good. Jeff, how about you? Any parting shots?
Jeff Amerine:
No, it’s great. It’s always great to have you on. I mean, I can remember back more than 10 years ago, when we were in the early days of this journey with the art challenge accelerator, and we had you come out to talk to that cohort and have been an admirer of your work and closely followed it and really appreciate all that you’ve done to help startup world and innovation in general. Appreciate having you on.
Ash Maurya:
Thank you.
Jeff Standridge:
Yeah. And if you came to the Arnold Innovation Center across the street, which we run, you would find that mantra on our wall in our, in our team room, appropriately attributed, I might add.
Ash Maurya:
Awesome. Well, that’s great to hear. Well, thanks for having me.
Jeff Standridge:
Well, yes, sir. Thank you. That’s been Ash Maurya: Maria with, uh, Lean Stack and Lean Foundry. Uh, a repeat guest with the Innovation Junkies Podcast. Check him out in his first episode, uh, episode, uh, season one, episode 23 on lean innovation. Ash, thank you so much for being with us. It’s been another episode of the Innovation Junkies Podcast. We’ll see you next time.